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METHOD OF SEED ESTIMATION FOR ELECTROMAGNETIC TRACKER USING A PRIORI INFORMATION

Publishing Venue

The IP.com Prior Art Database

Abstract

In an embodiment, this invention applies the concept of using a priori information for improving the quality of initial seed. A specific seed calculation method for mobile C-arm navigation is disclosed here to prove the concept.

Country

Undisclosed

Language

English (United States)

This text was extracted from a Microsoft Word document.

At least one non-text object (such as an image or picture) has been suppressed.

This is the abbreviated version, containing approximately
39% of the total text.

METHOD OF SEED ESTIMATION FOR ELECTROMAGNETIC TRACKER USING A
PRIORI INFORMATION

BACKGROUND OF THE INVENTION

[0001]
This
invention discloses a two-step seed algorithm for improving robustness of the
new tracking method. Since the two-step
seed algorithm is essentially a numerical table-lookup based approach, it may
not work effectively and reliably for the entire tracking space. The
experimental results have shown that the iterative tracking algorithm may not
always coverage to a stable position and orientation (P&O) solution for the
tracker positions where the initial seeds significantly deviate from the actual
sensor position. Usually the diverged
P&O output can be detected by some kind of accuracy checking mechanism and
removed from navigation data set to avoid patient injury. However, it does not
restore the navigation capability at those locations. Thus, an alternative fix
for inaccurate seed estimation is needed in addition to the proposed 2-step
seed searching method.

DETAILED DESCRIPTION OF THE INVENTION

A priori information is very useful for
solving inverse problems where the iterative parameter minimization methods are
commonly used. For example, it is a popular approach in non-linear image
reconstruction to use knowledge of anatomical properties to find high quality
seeds for the algorithm [3].

In some specific navigation applications such as the mobile
C-arm tracking system, the receiver is permanently attached to C-arm and moved
together with the gantry on a well-controlled spatial path. To correlate the
known C-arm motion information with the tracker data, we can largely improve
the robustness of the tracking algorithm by having good initial guess of the
receiver position to launch the fitter.

Using Bonneville 3D navigation system as an example, the
design requires a highly repeatable motion control of the Flat Panel Detector
(FPD) to sweep around the patient on the orbital plane. As illustrated in Figure 1, a receiver (Rx) pack is rigidly attached to the FPD, and a
transmitter (Tx) is affixed to patient anatomy and remains the same location
and pose with respect to the world coordinate system (WCS) during the 3D sweep.
If we assume there is no C-arm deflection, we may fit the Rx positions to a
perfect circle based on a subset of tracker data acquired during the C-arm
sweep. The tracker data used for circle
fitting should be those converged outputs from the field mapping program. The
goal is to estimate the missing Rx positions that the fitter does not work
because of inaccurate seeds.

To do this, a Gauss-Newton algorithm is used to find estimate
of rotation and translation parameters that transform the data to the best-fit
circle. As a result, we may obtain the rotation center (x0, y0,
z0), radius R, and normal direction of the fitted circle.
A patient coordinate system (CS) is then defined at the rotation center having
the same axis directions as the Tx CS.